Associative and Time Series Forecasting Models

All businesses are confronted with the general problem of having to make decisions under conditions of uncertainty. Management must understand the nature of demand and competition in order to develop realistic business plans, determine a strategic vision for the organization, and determine technology and infrastructure needs. To address these challenges, forecasting is used. According to Makridakis (1989), forecasting future events can be characterized as the search for answers to one or more of the following questions:

It helps clarify relationsips between variables that cannot be examined by other methods and allows prediction

* Forecasting is an impartial strategic ingredient that will ensure apt base for reputable planning. Our forecast is always the first step in developing plans in running the business along with our future plans of growth strategies. With this tool, we are able to anticipate our sales within reason that then can allow for us to control our costs in conjunction with inventory which will then help us to enhance our customer service. Sales forecasting is a vital strategic tactic in our company’s methodology.

2. Use regression to develop a trend line that could be used to forecast monthly

3. Market Share: forecasting will help in identifying the size of the market share and market potential will aid in the manufacturing and distribution process. Will also aid in proper utilization and eliminate waste.

Regression analysis will be performed on all variables to determine if relationships exist between variables.

CorrelationalIdentify relationships and how well one variable predicts another. Helps clarify relationships between variables that cannot be examined by other methods and allows prediction.Researchers cannot identify cause and effectStatistical analysis of relationship between variables.

Forecasting is the methodology utilized in the translation of past experiences in an estimation of the future. The German market presents challenges for forecasting techniques especially for its retail segment. Commercially oriented organizations are used to help during forecasting as general works done by academic scientists are not easy to come across (Bonner, 2009).

Forecasters use these mental maps to organize their observations of directional information. Since innovations rarely apply to the entire marketplace, information must be tagged for the appropriate price point, category and classification. In this way, forecasters turn random bits of data into useful information for decision support, points and style directions.

Predictive Analytics. This type of business analytics is to answer the question “What is likely to happen?”. The process of predictive analytics is identifying past patterns and using statistical models and forecast techniques to understand the future. The output of the predictive analytics, for example is, which age range of customers that will apply for credit cards in the next 12 months, which income range of the customers that will use the electronics banking facilities for next 2 years.

The purpose of this report is to demonstrate the forecasting power of statistical data analytics. We will use a time series dataset to conduct the forecasting, since this type of datasets contain a set of observations generated sequentially in time. Organizations of all types and sizes utilize time series datasets for analysis and forecasting for predicting next year 's sales figures, raw material demand, monthly airline bookings, etc. A time series model is useful to obtain an understanding of the underlying forces and structure that produced the data.

Predictive analytics is a technology that makes use of the current and existing database to produce a trend and predict future outcomes. In short, it does not tell you what will happen in the future.

Forecast, as the Oxford Advanced Learner’s Dictionary defined, is “a statement about what will happen in the future, based on the information that is available now”. A scientific one is making forecast by a scientific method, which is defined by Merriam-Webster Collegiate Dictionary as:

The time series is a group views sorted by time (and often time periods equal and successive periods vary according to the nature of this phenomenon). And time series have important applications in many areas, including economic, trade and population statistics. As time-series models are typically used to predict the variable values. If the variable to be studied is known determinants, and the factors that affect it, is also used in the case of variable is subject to the expectations of its clients, which is reflected in the future based on what happened in the past. Mathematically: we say that the independent variable time (t) and the corresponding values him dependent variable (y) and that each value at time t corresponding values of y variable y is a function of time t in which: y = F (t), The time series analysis of statistical methods task method, which has evolved a lot, and it was possible to use it for the purpose of expectation for the future supply and demand for a commodity or service. And supports time-series analysis to track the phenomenon style (or variable) over a certain time (several years, for example), then expect for the future based on different values that have emerged in the time series and the pattern of growth in values; and this is superior to the conventional method, since the method traditional calculates the difference in value between the only two date ranges of the time series and builds future expectation on the basis of which, without

Use for Forecasting – The impact of external forces makes it difficult to use the PLC as a forecasting tool. For instance, market factors not directly associated with the marketing activities of market competitors, such as economic conditions, may have a greater impact on reducing demand than customers’ interest in the product. Consequently, what may be forecasted